image analytics in healthcare

Data Science for Healthcare: Image Analytics

May 2, 2023

© AlgoAnalytics All rights reserved

Outline

Other Work in Healthcare

Image Analytics Work in Healthcare

Machine Learning and Healthcare

About AlgoAnalytics


CEO and Company ProfileAniruddha PantCEO and Founder of AlgoAnalytics

PhD, Control systems, University of California at Berkeley, USA 2001

• 20+ years in application of advanced mathematical techniques to academic and enterprise problems.

• Experience in application of machine learning to various business problems.

• Experience in financial markets trading; Indian as well as global markets.

Highlights

• Experience in cross-domain application of basic scientific process.

• Research in areas ranging from biology to financial markets to military applications.

• Close collaboration with premier educational institutes in India, USA & Europe.

• Active involvement in startup ecosystem in India.

Expertise

• Vice President, Capital Metrics and Risk Solutions• Head of Analytics Competency Center, Persistent Systems• Scientist and Group Leader, Tata Consultancy Services

Prior Experience

• Work at the intersection of mathematics and other domains

• Harness data to provide insight and solutions to our clients

Analytics Consultancy

• +30 data scientists with experience in mathematics and engineering

• Team strengths include ability to deal with structured/ unstructured data, classical ML as well as deep learning using cutting edge methodologies

Led by Aniruddha Pant

• Develop advanced mathematical models or solutions for a wide range of industries:

• Financial services, Retail, economics, healthcare, BFSI, telecom, …

Expertise in Mathematics and Computer Science

• Work closely with domain experts – either from the clients side or our own – to effectively model the problem to be solved

Working with Domain Specialists

About AlgoAnalytics

AlgoAnalytics - One Stop AI Shop

Healthcare• Medical Image diagnostics• Work flow optimization• Cash flow forecasting

Financial Services• Dormancy prediction• Recommender system • RM risk analysis• News summarization

Retail• Churn analysis• RecSys• Image recognition• Generating image description

Others• Algorithmic trading strategies• Risk sensing – network theory• Network failure model• Clickstream analytics• News/ social media analytics

Aniruddha PantCEO and Founder of AlgoAnalytics

•We use structured data to design our predictive analytics solutions like churn, recommender sys

•We use techniques like clustering, Recurrent Neural Networks,

Structured data

•We used text data analytics for designing solutions like sentiment analysis, news summarization and many more

•We use techniques like natural language processing, word2vec, deep learning, TF-IDF

Text data

• Image data is used for predicting existence of particular pathology, image recognition and many others

•We use techniques like deep learning – convolutional neural network, artificial neural networks and technologies like TensorFlow

Image data

•We use sound data to design factory solutions like air leakage detection, identification of empty and loaded strokes from press data, engine-compressor fautl detection

•We use techniques like deep learning

Sound Data


Technology:


Healthcare: Areas of Analytics Application

Patient Aid – medical diagnostics

Revenue Cycle Management

Resource Allocation

What kind of proposals have higher chance of getting funded


Machine Learning for Healthcare: Overview

Medical diagnostics – Detecting serious disorders or diseases through image analytics and medico-genomic data

Reducing Readmissions – using scoring techniques to locate high risk customers and preventing readmissions through higher monitoring

Population Health Management - Uses huge amount of past patient data/ genomics data for diagnosing high risk group through risk stratification score.

Cash Flow Forecasting – Forecasting of cash flows based on claims history, reimbursement analysis and potential denials to forecast cash

Billing Errors– Identify opportunities to collect missing income, including are wrongfully rejected claims by payers or overdue money from patients.

Patient Selection - Insurance coverage, Identifying patients those are not likely to pay in full

Work Flow Optimization– Using historical data for staffing to reduce costs, Having the right clinician at right time at right place

Efficient Use of Hospital Resources – Prevent bottlenecks in urgent care by analyzing patient flow during peak times

Fraud and abuse prevention - Cost trending-forecasting, care utilization analysis, actuarial and financial analysis are commonly used applications for preventing frauds

Trend Analysis - Tracking hospital’s market position and competitive trends. And visualizing changes in market dynamics and growth patterns..

Grant problem - Predict likelihood that a particular proposal will receive grant using text analytics


IMAGE ANALYTICS WORK IN HEALTHCARE


AlgoAnalytics Image Analytics Expertise: Methods and Technologies

Methods TechnologiesStatistical Learning

Artificial Neural Network

Convolutional Neural Network

Transfer Learning (Deep Learning)

TensorFlow-Python for neural networks (feed-forward and CNN)

R Programming for statistical models: using pixel values as features and applying models such as logistic regression, random-forest

Google’s Inception model (pre-trained TensorFlow model on Imagenet dataset)

• In healthcare segment, classification algorithms help predict whether a particular pathology exists or not.

• Image classification can be used in various solutions like•Diabetic ophthalmology•Brain MRI scan•Skin disease diagnosis

Image Classification

•Segmentation is the process of dividing image into regions with similar properties

•Medical image segmentation aims at studying anatomical structures, locate tumour, lesions or other abnormaliaties

•Some applications•Retinal vein segmentation, •Tumour segmentation•Annuyrism detection•Lung cancer, breast cancer, hepatocelular carcinoma

Image segmentation

•Gives description of what is occuring on the given a set of images, with its caption, create a predictive model which generates relevant caption for the unseen images.•We use a combination of deep convolutional neural network and LSTM language modeling (RNN) to generate captions

•This can be used in automated report generation from image analytics

Image description


Diabetic Retinopathy Brain MRI Scan

Image Classification - Healthcare


Chest Xray Diagnostics - predict whether it is normal or nodular

Input X-ray imagesVGGNet-16 Extracted featureset vectors

Deep learning

Featureset generation

Logistic regression

Classification

Nodular (class -1) Vs. Normal(class -0) X-ray

Results

● Classification Accuracy : 75% ● AUC : 81.29%● Kappa Score : 47.3 %

● Deep Learning Techniques : ○ Maxpool-5 layer of a VGGNet-16 deep learning neural net to

extract features from the X-rays.○ Machine Learning algorithms (Logistic regtression, Random

forest) to perform classification of the X-rays into 2 classes - 0 as Normal and 1 as abnormal.

Dataset : The dataset consists of Images made available by the Department

of Health and Human Services of Montgomery County, MD, USA.

138 posterior-anterior x-raysOut of which, 80 are normal X-rays and 58 x-rays are abnormal with

manifestations of tuberculosis.The set covers a wide range of abnormalities

Image Classification - Healthcare


Image Segmentation - Automatic Brain Tumor Segmentation Dataset :

- All MRI data was provided by the 2015 MICCAI BraTS Challenge, which consists of approximately 250 high-grade glioma cases and 50 low-grade cases.

- Each dataset contains four different MRI pulse sequences, each of which is comprised of 155 brain slices with each pixel representing a 1mm3 voxel, i.e., total of 620 images per patient. An example of a scan with the ground truth segmentation

with the segmentation labels

Original Image

Normalized Image

Final N4 Bias corrected Image

Input preprocessing

Methodology : - Applied normalization and n4ITK bias correction on the original input images .- Generated 43264 patches per image for the input to the 4 layered Convolutional Neural Network (CNN) model.- Classification of tumor area based on 4 categories viz., edema, advancing tumor, non –advancing tumor and necrotic

tumor core.

Prediction Process using CNN modelResults with segmented tumor


Segmentation of blood vessel in retina Microaneurysms in color fundus image

Image Segmentation

Objective : Microaneurysms (MA) segmentation for early diabetic retinopathy screening using fundus imagesDataset : Publicly available Retinopathy Online Challenge (ROC) dataset (total 50 fundus images) with pixel locations and radius of microaneurysmsDeep Learning Approach :

- Creating smaller patches of input images where center pixel is either MA or Non-MA

- Predicting the probability of center pixel being MA using Convolutional Neural Network (CNN) model

- Using trained CNN model to locate MAs in unseen fundus image


Image Segmentation - Deep Learning for Ultrasound Nerve Segmentation

Problem Statement: Accurately identifying nerve structures in ultrasound images for effectively inserting a patient’s pain management catheter.

U-Net U-Net Trained Model

InputData

Ultrasound Image

Mask

Test Image

Predicted Mask

Process:Step 1 - Train neural network on given ultrasound nerve images and their masksStep 2 - Use trained model to predict the mask for new test image

Results on 500 test images: Dice Coefficient (pixelwise accuracy) = ~72%


Benefits of Image Analytics in Healthcare

Provides real-time insight to healthcare providers during diagnosis and treatment

Improves the standard of care and speed of diagnosis, treatment and recovery

Reduces varying subjective interpretation and human error

Offers the potential to make faster and more informed decisions, streamline costs and broadly improve the quality and economics of healthcare

Prediction of certain genetic disorders, monitor patients at risk

Image Analytics in healthcare current estimated US market size is at $141 million and expected to grow at 8% CAGR

The Benefits include:

Interested in knowing more?Contact us: [email protected]

May 2, 2023

image analytics in healthcare

Data & Analytics